A metric for determining the significance of failures and its use in anomaly detection case study: Mobile network management data from lte network

Robin Babujee Jerome*, Kimmo Hätönen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

In big data analytics and machine learning applications on telecom network measurement data, accuracy of findings during the analysis phase greatly depends on the quality of the training data set. If the training data set contains data from Network Elements (NEs) with high number of failures and high failure rates, such behavior will be assumed as normal. As a result, the analysis phase will fail to detect NEs with such behavior. High failure ratios have traditionally been considered as signs of faults in NEs. Operators use wellknown Key Performance Indicators (KPIs), such as, e. g., Drop Call Ratio and Handover failure ratio to identify misbehaving NEs. The main problem with these KPIs based on failure ratios is their unstable nature. This paper proposes a method of measuring the significance of failures and its use in training set filtering.

Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks - 16th International Conference, EANN 2015, Proceedings
PublisherSpringer Verlag
Pages171-180
Number of pages10
Volume517
ISBN (Print)9783319239811
DOIs
Publication statusPublished - 2015
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Engineering Applications of Neural Networks - Rhodes, Greece
Duration: 25 Sep 201528 Sep 2015
Conference number: 16

Publication series

NameCommunications in Computer and Information Science
Volume517
ISSN (Print)1865-0929

Conference

ConferenceInternational Conference on Engineering Applications of Neural Networks
Abbreviated titleEANN
CountryGreece
CityRhodes
Period25/09/201528/09/2015

Keywords

  • Anomaly detection
  • Pre-processing
  • Self-Organizing maps
  • Training set filtering

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